Artificial Sets Research Articles

Emergent behaviors of buckling-driven elasto-active structures

Active systems of self-propelled agents, e.g., birds, fish, and bacteria, can organize their collective motion into myriad autonomous behaviors. Ubiquitous in nature and across length scales, such phenomena are also amenable to artificial settings, e.g., where brainless self-propelled robots orchestrate their movements into spatial-temporal patterns via the application of external cues or when confined within flexible boundaries. Like their natural counterparts, these approaches typically require many units to initiate collective motion, so controlling the ensuing dynamics is challenging. Here, we demonstrate a simple mechanism that leverages nonlinear elasticity to tame near-diffusive motile particles in forming structures capable of directed motion and other emergent behaviors. Our elasto-active system comprises two centimeter-sized self-propelled microbots connected with elastic beams. These microbots exert forces that suffice to buckle the beam and set the structure in motion. We first rationalize the physics of the interaction between the beam and the microbots. Then we use reduced-order models to predict the interactions of our elasto-active structures with boundaries, e.g., walls and constrictions, and demonstrate how they can exhibit remarkable emergent behaviors such as maze navigation. These findings demonstrate that allowing and understanding changes in body morphology can enhance the capabilities of active matter systems and enable the design of robotic materials capable of space exploration, adaptation, and complex interactions with their surrounding environment.

Interproximal tooth cleaning operated by a tactile robot. An in vitro analysis.

New technologies such as tactile robots and artificial intelligence (AI) are finding their way into clinical practice in dentistry and could contribute to the improvement of oral health care in the future. The hypothesis of the present in vitro pilot study trial was that a collaborative tactile robot programmed by a dental student could remove interproximal artificial plaque as effectively as a human operator. Model teeth were fully covered with artificial plaque and set into phantom jaws. First, a robot was programmed by a dental student to perform interproximal cleaning with an interproximal brush. Second, the teeth were covered with artificial plaque again, and the dental student performed the interproximal cleaning manually. Both experiments were repeated five times. Residual plaque was measured with binary pictures. Surface coverage was reported, and the comparison of methods was performed with significance defined at α = 0.05. No statistically significant difference was found in the cleaning result between the robot and the human operator. The results of the present in vitro pilot study indicate that a tactile robot with integrated AI programmed by a dental student can perform interproximal cleaning as effectively as a dental student. In the future, the use of robot assistants to support oral hygiene, eg, in patients with reduced motor skills or impaired vision, should be fur ther investigated.

Sacred Meets Synthetic: A Multi-Method Study on the First AI Church Service

Artificial Intelligence (AI) is transforming society, including religious practices and experiences of religious communities. The first large-scale AI church service took place in 2023 in Germany as a Protestant Christian worship event within a biannual religious celebration. Studying this religious service illustrates how AI was developed as a religious agent and provides insights into the experiences of attendees. This research note presents data from a quantitative survey and two qualitative questionnaires of participants conducted at the AI worship service. Findings show diversity of opinion and a range of attendee experiences. By presenting the results of qualitative and quantitative analysis, this research note highlights the possibilities and limitations of incorporating AI into the religious sphere. While most of the responses were skeptical, participants also reported having spiritual experiences. Nevertheless, attendees also reported conflicting feelings regarding experiences of community within an artificial setting. The main concerns were technological limitations, fear of replacing humans, biases in the theology of the underlying large language model, and lack of personality and emotion. Age-based differences include younger attendees finding the AI service more attractive while older attendees found the AI service more stimulating. Implications are drawn for practical theology and AI implementation within religious settings.

A Study on the Application of Online Corpora in Grammar Teaching

Traditional grammar teaching in higher education often disconnects from real-life contexts or is conducted in imagined, artificial settings. Compared to traditional teaching methods, utilizing online corpora, which contain vast amounts of authentic language, offers significant advantages for teaching. This study focuses on grammar, using Larsen-Freeman's three-dimensional grammar teaching theory as the conceptual basis for instructional processing, and discusses the author's attempts to apply corpora in grammar teaching. The significance of this teaching innovation lies in enhancing students' grammatical competence and exploring the feasibility of integrating corpus concepts and technologies into instruction.

Is it me or is it you? Physiological effects of the honey bee microbiota may instead be due to host maturation.

Microbiota-mediated impacts on host physiology and behavior have been widely reported in honey bees (Apis mellifera). However, most of these studies are conducted in artificial lab settings and fail to take into account, or make incorrect assumptions about, the complex physical and social structures inherent to natural hive conditions. A new study by Liberti et al. (J. Liberti, E. T. Frank, T. Kay, L. Kesner, et al., mBio 15:e01034-24, 2024, https://doi.org/10.1128/mbio.01034-24) identifies one such overlooked aspect-the behavioral maturation from nurses to foragers-that can be a serious confounding factor in bee microbiota experiments. Using cuticular hydrocarbon profiling to discern between the two maturation states, they find that multiple physiological and behavioral differences between age-matched lab bees could potentially be explained by their maturation state instead of the intended treatment conditions, such as microbial inoculation. This study serves as a stark wake-up call on the necessity of careful replication and cross-disciplinary knowledge transfer (e.g., between animal specialists and microbiologists) in order to truly understand complex host-microbe systems.

An automated social media manager based on artificial intelligence

In today's world of social media, where brands and companies constantly interact with their audiences, the automation of content management processes is becoming increasingly important. Manual content creation, publication, and analysis on social networks require significant resources and time. Automated social media management systems based on artificial intelligence help simplify these processes, enhancing efficiency and optimizing audience engagement strategies. Artificial intelligence in social media marketing systems allows for the automation of tasks such as creating text posts, scheduling publications across different platforms, and analyzing user interactions. Artificial intelligence can analyze audience behavior, predict the optimal time for posts, and recommend changes to content strategies for maximum reach. This not only saves time but also improves the effectiveness of marketing campaigns through more accurate analytical data. However, the automation of social media marketing also faces challenges. The primary issues include the complexity of integrating different social networks into a single management system and the limitations in generating high-quality content that meets the emotional and stylistic requirements of the brand. Additionally, a major challenge is content personalization based on data analysis, which must account for each audience segment's unique needs and behavioral characteristics. Despite these challenges, the prospects for developing automated social media marketing managers are promising due to ongoing innovations in artificial intelligence and machine learning. Future research and development will focus on improving the quality of automated content generation, enhancing user interfaces, and integrating new social platforms to expand multi-channel marketing campaign management capabilities. Thus, the automation of social media marketing is a promising field that will enable brands and companies to optimize their audience engagement strategies while significantly reducing the time and resources required for content creation, publication, and analysis. This work presents the structure of the main entities of an automated social media management system based on artificial intelligence, including users, professions, questions, answers, settings, and integration with Telegram. The developed system enables efficient content management, automates publications, and personalizes audience interaction based on user responses. The system's described key components, attributes, and relationships provide scalability and flexibility, allowing brands and companies to optimize their marketing strategies and client interactions.

Artificial Cultivation of Aquatic Plants Promotes Nitrogen Transformation and the Abundance of Key Functional Genes in Agricultural Drainage Ditch Sediments in the Yellow River Irrigation Area in China

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. We here compare key processes of nitrogen transformation occurring in the rhizosphere of sediments of a ditch artificially planted with a mix of species (Phragmites australis, Typha orientalis, Nymphaea tetragon) with the rhizosphere of a ditch occupied by naturally occurring aquatic vegetation, dominated either by P. australis or T. orientalis. Our results revealed a species effect, with an increased denitrification rate (DR) and dissimilatory nitrate reduction to ammonium rate (DNRAR) in the cultivated ditch for P. australis, compared to the naturally occurring T. orientalis vegetation. The nitrogen fixation rate (NFR) increased in the artificial setting with T. orientalis in comparison to natural P. australis vegetation. The richness of the bacterial community and the relative abundances of Bacteroidota, Firmicutes, and Geobacter were significantly greater in the rhizosphere of the artificially cultivated ditch due a greater availability in nitrogen and organic carbon. In the artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, whereas DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels. The dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK, and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

Physicochemical Hydrodynamics of Particle Diffusiophoresis Driven by Chemical Gradients

Chemical gradients, the spatial variations in chemical concentrations and components, are omnipresent in environments ranging from biological and environmental systems to industrial processes. These thermodynamic forces often play a central role in driving transport processes taking place in such systems. This review focuses on diffusiophoresis, a phoretic transport phenomenon driven by chemical gradients. We begin by revisiting the fundamental physicochemical hydrodynamics governing the transport. Then we discuss diffusiophoresis arising in flow systems found in natural and artificial settings. By exploring various scenarios where chemical gradients are encountered and exploited, we aim to demonstrate the significance of diffusiophoresis and its state-of-the-art development in technological applications.

Quantifying gliding forces of filamentous cyanobacteria by self-buckling

Filamentous cyanobacteria are one of the oldest and today still most abundant lifeforms on earth, with manifold implications in ecology and economics. Their flexible filaments, often several hundred cells long, exhibit gliding motility in contact with solid surfaces. The underlying force generating mechanism is not yet understood. Here, we demonstrate that propulsion forces and friction coefficients are strongly coupled in the gliding motility of filamentous cyanobacteria. We directly measure their bending moduli using micropipette force sensors, and quantify propulsion and friction forces by analyzing their self-buckling behavior, complemented with analytical theory and simulations. The results indicate that slime extrusion unlikely generates the gliding forces, but support adhesion-based hypotheses, similar to the better-studied single-celled myxobacteria. The critical self-buckling lengths align well with the peaks of natural length distributions, indicating the importance of self-buckling for the organization of their collective in natural and artificial settings.

Quantifying gliding forces of filamentous cyanobacteria by self-buckling.

Filamentous cyanobacteria are one of the oldest and today still most abundant lifeforms on earth, with manifold implications in ecology and economics. Their flexible filaments, often several hundred cells long, exhibit gliding motility in contact with solid surfaces. The underlying force generating mechanism is not yet understood. Here, we demonstrate that propulsion forces and friction coefficients are strongly coupled in the gliding motility of filamentous cyanobacteria. We directly measure their bending moduli using micropipette force sensors, and quantify propulsion and friction forces by analyzing their self-buckling behavior, complemented with analytical theory and simulations. The results indicate that slime extrusion unlikely generates the gliding forces, but support adhesion-based hypotheses, similar to the better-studied single-celled myxobacteria. The critical self-buckling lengths align well with the peaks of natural length distributions, indicating the importance of self-buckling for the organization of their collective in natural and artificial settings.

Overcooling in UAE homes: health issues and implications for learning efficiency

From 1990 to 2016, , air conditioning usage in the UAE increased from 25 to 125 terawatt hours, making up 70% of the country's electricity consumption. Overcooling to 18°C in homes is common, leading to health issues and reduced learning efficiency during the Covid-19 pandemic. This study uses Electroencephalograms (EEG) to examine indoor thermal environments' impact on learning efficiency. An artificial climate chamber set at 24°C or 20.5°C monitored 64 healthy males aged 21–29 during a 70-minute EEG session. Participants took a Visual Continuous Performance test and had their academic performance evaluated. Seven brain areas were analyzed for attention and power frequency. Results showed lower temperatures (20.5°C) increased EEG frequency, indicating better attention than 24°C. Statistical analysis revealed that concentration was achieved faster at lower temperatures. This study highlights the importance of optimizing indoor temperatures for improved learning efficiency and well-being.

Dimensionless dynamics: Multipeak and envelope solitons in perturbed nonlinear Schrödinger equation with Kerr law nonlinearity

In this work, the dimensionless form of the improved perturbed nonlinear Schrödinger equation with Kerr law of fiber nonlinearity is solved for distinct exact soliton solutions. We examined the multi-wave solitons and rational solitons of the governing equation using the logarithmic transformation and symbolic computation using an ansatz functions approach. Multi-wave solitons in fluid dynamics describe the situation in which a fluid flow shows several different regions (or peaks) of high concentration or intensity of a particular variable (e.g., velocity, pressure, or vorticity). Multi-wave solitons in turbulent flows might indicate the existence of several coherent structures, like eddies or vortices. These formations are areas of concentrated energy or vorticity in the turbulent flow. Understanding how these peaks interact and change is essential to comprehending the energy cascade and dissipation in turbulent systems. Furthermore, a sub-ordinary differential equation approach is used to create solutions for the Weierstrass elliptic function, periodic function, hyperbolic function, Chirped free, dark-bright (envelope solitons), and rational solitons, as well as the Jacobian elliptic function, periodic function, and rational solitons. Also, as the Jacobian elliptic function's' modulus m approaches values of 1 and 0, we find trigonometric function solutions, solitons-like solutions, and computed chirp free-solitons. Envelope solitons can arise in stratified fluids and spread over the interface between layers, such as layers in the ocean with varying densities. Their research aids in the management and prediction of wave events in artificial and natural fluid settings. In fluids, periodic solitons are persistent, confined wave structures that repeat on a regular basis, retaining their form and velocity over extended distances. These structures occur in a variety of settings, including internal waves in stratified fluids, shallow water waves, and even plasma physics.

Low Inter-Rater Reliability and Reproducibility of Neck Reflex/“Adler-Langer” Points in Neural Therapy Diagnostics but Increased Pressure Pain Threshold after Therapy: Results of a Randomized Controlled Observer-Blind Trial

Background: Neck reflex points or Adler-Langer points are commonly used in neural therapy to detect so-called interference fields. Chronic irritations or inflammations in the sinuses, teeth, tonsils, or ears are supposed to induce tension and tenderness of the soft tissues and short muscles in the upper cervical spine. The individual treatment strategy is based on the results of diagnostic Adler-Langer point palpation. This study investigated the inter- and intra-rater reliability and explored treatment effects. Methods: We performed a randomized controlled trial with 104 inpatients (80.8% female, 51.8 ± 12.74 years) of a German department for internal and integrative medicine. Patients were randomized to individual neural therapy according to the pathological findings (n = 48) or no treatment (n = 56). In each patient, three experienced raters (20–45 years of experience in neural therapy) and two novice raters (medical students) rated Adler-Langer points rigidity on a standardized rating scale (“strong,” “weak,” “none”). The patients independently evaluated the tenderness on palpation of the eight points using the same scale. Pressure pain thresholds were assessed at the eight Adler-Langer points. All patients were retested after 30 min. The five raters were blinded to treatment allocation and assessments of the other raters. Video recordings were obtained to assess the consistency of the areas tested by the different raters. Results: Agreement between patients and raters (Cohen’s kappa = 0.161–0.400) and inter-rater reliability were low (Fleiss kappa = 0.132–0.150). Moreover, the individual agreement (pre-post comparisons in untreated patients) was similarly low even in experienced raters (Cohen’s kappa = 0.099–0.173). Video documentation suggests that raters do not place their fingers in the correct segments (percentage of correct position: 42.0–60.6%). Pressure pain thresholds at five of the eight Adler-Langer points showed significant changes after treatment compared to none in the control group. Conclusion: Under this artificial experimental setting, this method of Adler-Langer point palpation has not proven to be a reliable diagnostic tool. But it could be shown that, as claimed by the method, the tenderness in five of eight Adler-Langer points decreased after neural therapy.

Graph deep learning recognition of port ship behavior patterns from a network approach

The detection and recognition of ship mobile behavior patterns based on automatic identification system (AIS) data are crucial for ship navigation and maritime supervision. However, present methods, such as rule-based constraints and density clustering extraction, still suffer from restrictions imposed by artificial settings of parameters and thresholds. The lack of neighborhood context information in unstructured and serialized ship trajectory vector data makes it challenging to incorporate advanced methods such as deep learning for pattern learning. This study builds multiple semi-supervised deep learning graph neural network (GNN) models based on the message passing paradigm for recognizing ship mobile behavior patterns by constructing an AIS trajectory network and computing multi-order node features through Delaunay triangulation. It can get rid of the dependence on parameters of traditional rule-based and unsupervised learning methods. It also reduces the dependence on auxiliary information in trajectory qualitative by introducing multi-order node features. We conducted the experiments on cargo ship data from New York and Singapore to test our method. The model's classification accuracy is more than 99% for the New York dataset and more than 95% for the Singapore dataset. This method can be extended to other types of ships and traffic trajectory vector data with the excellent application potential of GNN in pattern recognition of vector mobile data. The code and experimental data are available at: https://github.com/destiny1103/DT-GNN.

Digital Justice: Possibilities and Limits

This article, using the experience of the recent judicial reform of the People’s Republic of China, demonstrates the possibilities for the development of digital justice (as a techno-socio-legal phenomenon) and their limits. Received a legal assessment of actualization, taking into account the current state of affairs of the legal heritage of the Soviet period. The main directions of the development of digital justice have been differentiated and the need to establish the limits for the implementation of this process has been justified. The benefits of traditional justice versus simulated digital justice have been demonstrated. The thesis is justified that the process of digitalization, as an objective phenomenon, does not imply an artificial external setting of the limits of development; at the same time, in relation to specific types of social activities, it is not only permissible, but also advisable to level the directions of implementation of digitalization elements. In this connection, the installation on the formation of non-digital justice is quite reasonable.

Artificial Intelligence-Based Expert Prioritizing and Hybrid Quantum Picture Fuzzy Rough Sets for Investment Decisions of Virtual Energy Market in the Metaverse

Artificial Intelligence-Based Expert Prioritizing and Hybrid Quantum Picture Fuzzy Rough Sets for Investment Decisions of Virtual Energy Market in the Metaverse

Natural frequencies and stability for Z-shaped fluid-conveying pipes with constrained-layer damping – Semi-analytic modeling with experimental validation

As the common fluid-conveying component, pipe structures are susceptible to excessive vibration due to internal fluid and external excitation. Constrained-layer damping (CLD) is commonly applied to suppress structural vibrations. However, effective modeling and vibration suppression analysis for Z-shaped fluid-conveying pipes are lacking to explore novel dynamic behaviors. This paper presents a dynamic model for analyzing the three-dimensional vibration of Z-shaped fluid-conveying pipes treated by partial CLD based on the semi-analytical method. Utilizing Timoshenko beam theory and calculating the centrifugal, Coriolis, and inertial force of fluids, the energy of straight and curved fluid-conveying pipes treated by CLD is derived. Based on the Lagrangian energy function, the equation of motion is obtained by using the Rayleigh-Ritz method. The improved artificial spring set is deployed to model the interaction of pipe sections and the non-uniform constraint distribution of clamps. Modal parameters are obtained by solving characteristic equations. The proposed method is verified by comparisons with references and modal experiment results. Frequency parameters, damping effects, and stability are analyzed under various fluid, geometric, and CLD parameters. In conclusion, a novel dynamic model is provided for analyzing the vibration and stability of the Z-shaped fluid-conveying pipe treated by CLD, which lays a foundation for vibration suppression research of fluid-conveying pipe systems.

Smarter in the city? Lizards from urban and semi-natural habitats do not differ in a cognitive task in two syntopic species.

Urbanization occurs at a global scale, imposing dramatic and abrupt environmental changes that lead to biodiversity loss. Yet, some animal species can handle these changes, and thrive in such artificial environments. One possible explanation is that urban individuals are equipped with better cognitive abilities, but most studies have focused on birds and mammals and yielded varied results. Reptiles have received much less attention, despite some lizard species being common city dwellers. The Italian wall lizard, Podarcis siculus, and the common wall lizard, Podarcis muralis, are two successful lizards in anthropogenic habitats that thrive in urban locations. To test for differences in a cognitive skill between urban and semi-natural environments, we investigated inhibitory control through a detour task in syntopic populations of the two species, across 249 lizards that were tested in partially artificial field settings. Sophisticated inhibitory control is considered essential for higher degrees of cognitive flexibility and other higher-level cognitive abilities. In this task, we confronted lizards with a transparent barrier, separating them from a desired shelter area that they could only reach by controlling their impulse to go straight and instead detour the barrier. We found no differences between lizards in urban and semi-natural environments, nor between species, but females overall performed better than males. Moreover, 48% of the lizards in our study did not perform a correct trial in any of the 5 trials, hinting at the difficulty of the task for these species. This study is among the first to address lizard cognition, through their inhibitory control, as a potential explanation for success in cities and highlights one should be careful with assuming that urban animals generally have enhanced cognitive performance, as it might be taxa, task, or condition dependent.

Optimizing CRISPR/Cas9 approaches in the polymorphic tunicate Ciona intestinalis

CRISPR/Cas9 became a powerful tool for genetic engineering and in vivo knockout also in the invertebrate chordate Ciona intestinalis. Ciona (ascidians, tunicates) is an important model organism because it shares developmental features with the vertebrates, considered the sister group of tunicates, and offers outstanding experimental advantages: a compact genome and an invariant developmental cell lineage that, combined with electroporation mediated transgenesis allows for precise and cell type specific targeting in vivo. A high polymorphism and the mosaic expression of electroporated constructs, however, often hamper the efficient CRISPR knockout, and an optimization in Ciona is desirable. Furthermore, seasonality and artificial maintenance settings can profit from in vitro approaches that would save on animals. Here we present improvements for the CRISPR/Cas9 protocol in silico, in vitro and in vivo. Firstly, in designing sgRNAs, prior sequencing of target genomic regions from experimental animals and alignment with reference genomes of C. robusta and C. intestinalis render a correction possible of subspecies polymorphisms. Ideally, the screening for efficient and non-polymorphic sgRNAs will generate a database compatible for worldwide Ciona populations. Secondly, we challenged in vitro assays for sgRNA validation towards reduced in vivo experimentation and report their suitability but also overefficiency concerning mismatch tolerance. Thirdly, when comparing Cas9 with Cas9:Geminin, thought to synchronize editing and homology-direct repair, we could indeed increase the in vivo efficiency and notably the access to an early expressed gene. Finally, for in vivo CRISPR, genotyping by next generation sequencing (NGS) ex vivo streamlined the definition of efficient single guides. Double CRISPR then generates large deletions and reliable phenotypic excision effects. Overall, while these improvements render CRISPR more efficient in Ciona, they are useful when newly establishing the technique and very transferable to CRISPR in other organisms.

2:1 Multiplexer, 1:2 De-multiplexer,2:4 Decoder and 4:2 Encoder Circuit Design with CMOS Technology Implementing with Artificial Neural Network with Verilog HDL Code for Output

The goal of this project is to create a De-multiplexer gate using a complementary metal oxide semiconductor (CMOS) and an artificial neural network.When designing any COMS circuit, we always keep in mind that the lowest possible cost should be the aim.In this work, the circuit was designed using a multilayer artificial neural network. We utilize weights to alter the value and treat negative values as inverters and neurons as transistors in our work.We are also developing Verilog-HdL code to easily apply the De-multiplexer for experimenting with an artificial neural network and set weights to acquire the desired results. The purpose of this project is to use an artificial neural network and a complementary metal oxide semiconductor (CMOS) to develop a decoder gate.We constantly keep in mind that the goal should be to construct a COMS circuit at the lowest feasible cost.An artificial neural network with multiple layers was used to create the circuit in this work. In our study, we treat negative values as inverters and neurons as transistors, and we modify the value using weights.Additionally, we are creating Verilog-HdL code that will make it simple to use the Decoder to experiment with artificial neural networks and adjust weights to get the desired outcomes. The purpose of this project is to use an artificial neural network and a complementary metal oxide semiconductor (CMOS) to develop a decoder gate.We constantly keep in mind that the goal should be to construct a COMS circuit at the lowest feasible cost.An artificial neural network with multiple layers was used to create the circuit in this work. In our study, we treat negative values as inverters and neurons as transistors, and we modify the value using weights.Additionally, we are creating Verilog-HdL code that will make it simple to use the Decoder to experiment with artificial neural networks and adjust weights to get the desired outcomes. The purpose of this research is to use an artificial neural network and a complementary metal oxide semiconductor (CMOS) to produce a multiplexer gate.We always keep in mind that the lowest feasible cost should be the goal while designing any COMS circuit.This work used a multilayer artificial neural network to design the circuit. In our work, we treat neurons as transistors and negative values as inverters, and we use weights to adjust the value.In order to effortlessly apply the Multiplexer for experimenting with an artificial neural network and set weights to obtain the desired results, we are also building Verilog-HdL code.Keywords: Multiplexer, CMOS,ANN,Verilog-HDL. Keywords: Multiplexer De-multiplexer, Decoder, Encoder Verilog-HDL, CMOS, ANN, Verilog HDL